ADaM (Adaptive Daisy Model): an R package for discriminating between core fitness and context-specific fitness genes in large-scale gene essentiality datasets.

The Adaptive Daisy Model (ADaM) package implements a semi-supervised algorithm for computing a fuzzy-intersection of non-fuzzy sets by adaptively determining the minimal number of sets to which an element should belong in order to be a member of the fuzzy-intersection (the membership threshold).

This threshold maximises the deviance from expectation of the cardinality of the resulting fuzzy-intersection, as well as the coverage of predefined elements.

This method can be used to identify the minimal number of cell lines from a given tissue in which the inactivation of a gene (for example via CRISPR-Cas9 targeting) should exert a reduction of viabilty (or fitness effect) in order for that gene to be considered a core-fitness essential gene for the tissue under consideration.

This method is used to discriminate between core-fitness and context-specific essential genes in a study describing a large scale genome-wide CRISPR-Cas9 pooled drop-out screening [1] (a detailed description of the algorithm is included in the Supplemental Information of [1]).

ADaM was inspired by the Daisy Model method introduced in [2]


[1] Behan FM & Iorio F & Picco G et al., In press.

[2] Hart T et al., High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell. 2015;163:1515–26.


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Sanger Institute Contributors

Photo of Dr Francesco Iorio

Dr Francesco Iorio

Principal Staff Scientist

Photo of Dr Clare Pacini

Dr Clare Pacini

Postdoctoral Fellow